Speaker
Description
Background: Antidepressant prescribing can act as a proxy for mental health conditions allowing spatial and statistical analysis to be undertaken at small area level. These data can complement national survey and hospital care data which are limited in size and typically available only at coarse geographies. This work aims to explore the spatial distribution of antidepressant prescribing in primary care data and examine associations with socio-demographic factors.
Methods: Prescribing count data were collected for Antidepressant drugs (BNF section 4.4) from openPrescribing.org. Data are at General Practitioner level and were downloaded for a 5-year period. Census 2021 data for deprivation, age, ethnicity and home ownership were downloaded from Nomisweb.co.uk. Antidepressant prescription rates were calculated at neighbourhood level using a GP catchment area to Lower Super Output Area lookup. Logistic regression was used to examine associations with prescription rates. Initial analysis was run for the case study area of Leeds, UK. This work provides a proof-of-concept study for national analysis.
Results: Antidepressant prescribing varied spatially within the study area. Positive statistically significant associations are found with social (OR=1.248), private (OR=1.269) and owned housing (OR=1.244), and Mixed ethnicity (OR=1.271). Households deprived in 2 dimensions (OR=1.078) and Asian ethnicity (OR=1.016) have weaker but statistically significant associations. Black ethnicity (OR=0.94) and rented households (OR=0.59) are associated with statistically significant negative associations.
Conclusions: This work provides evidence of spatial variations in antidepressant prescribing patterns and finds associations between higher rates of prescriptions and some socio-demographic groups but further work around GP prescribing behavior is required.
